Software Development Engineer

Job requirements

Proven experience building and shipping full-stack applications in a professional product-based environment.

Ability to independently own and deliver features or systems across sprint/release cycles.

Mandatory Skills & Responsibilities

Strong hands-on experience with at least one modern backend stack, such as Java, Python, or Node.js/NestJS, with solid understanding of performance tuning, memory management, and concurrency.

Strong experience with modern frontend development using React and SSR-based frameworks where applicable, including routing, state management, and performance optimization.

Working knowledge of a major cloud platform such as GCP or Azure, including production deployments, containers, serverless services, messaging, IAM, and cost-aware operations.

Strong SQL fundamentals, including query optimization, indexing, schema design, and data modeling, with working knowledge of at least one NoSQL database.

Proven ability to design scalable and resilient systems using caching, load balancing, and event-driven architecture.

Comfortable working in an Agile/Scrum environment, with a track record of independently delivering features across sprints and releases.

Strong Git practices, including sensible branching strategies and clean release management.

Experience designing REST and/or GraphQL APIs, including versioning and backward compatibility.

Strong testing mindset, with experience in unit, integration, and contract testing.

Hands-on experience with Docker and container orchestration platforms such as Kubernetes.

AI Skills:

Comfortable using AI tools in day-to-day engineering work to move faster, work smarter, and stay cost-aware while doing it

Familiarity with Retrieval Augmented Generation (RAG) architectures — semantic layers for data retrieval and grounding LLM responses in real, trustworthy data

Practical experience with at least one LLM application framework (LangChain, LangGraph, Google GenAI tools, or similar) for prototyping and integrating AI-driven features

Exposure to agentic or multi-agent AI systems — agent roles, tool use, memory, and workflow orchestration.